Information processing apparatus, information processing method, and program

ABSTRACT

An information processing method is disclosed which includes the steps of: (a) calculating levels of similarity between a standard form vector as a comparison basis vector and each of a plurality of item characteristic vectors of the standard form which are characteristic of a plurality of items, the standard form vector being made up of N components individually representative of N attributes of each of the plurality of items, N being an integer of at least 1, step (a) further determining as the items to be recommended to a user a plurality of items corresponding to the item characteristic vectors of which the level of similarity satisfies a first condition; (b) determining one of the N attributes of the plurality of recommended items determined in step (a) as a common recommendation reason common to the plurality of recommended items when the determined attribute satisfies a second condition; and (c) controlling presentation to the user of either the plurality of recommended items determined in step (a) or information about the recommended items, together with the common recommendation reason determined in step (b).

CROSS REFERENCES TO RELATED APPLICATIONS

The present invention contains subject matter related to Japanese PatentApplication JP 2005-002088 filed with the Japanese Patent Office on Jan.7, 2005, the entire contents of which being incorporated herein byreference.

BACKGROUND OF THE INVENTION

The present invention relates to an information processing apparatus, aninformation processing method, and a program. More particularly, theinvention relates to an information processing apparatus, an informationprocessing method, and a program for presenting users with recommendeditems together with convincing reasons for the recommendation.

Systems for recommending items to users (simply called therecommendation system hereunder) have gained widespread use as onevariation of information processing systems in recent years (e.g., seeJapanese Patent Laid-Open No. 2004-194107). An item refersillustratively to a product, commodity, etc., that may be purchased byusers. In the above-cited patent document, items are referred to ascontent. The items will be discussed later in detail.

Some of the recommendation systems proposed so far are know to presentusers with recommended items along with reasons for the recommendation.

SUMMARY OF THE INVENTION

Such traditional recommendation systems including the one disclosed inthe above-cited application have one disadvantage in common: they havehad difficulty in presenting the user with convincing reasons forrecommendation. In other words, there exist quite a few users who findit incomprehensible why such and such items are presented as somethingrecommendable by the system.

The present invention has been made in view of the above circumstancesand provides arrangements which, when presenting the user withrecommended items, can present at the same time convincing reasons forthe recommendation.

According to one embodiment of the present invention, there is provideda first information processing apparatus including: recommending meansfor calculating levels of similarity between a standard form vector as acomparison basis vector and each of a plurality of item characteristicvectors of the standard form which are characteristic of a plurality ofitems, the standard form vector being made up of N componentsindividually representative of N attributes of each of the plurality ofitems, N being an integer of at least 1, the recommending means furtherdetermining as the items to be recommended to a user a plurality ofitems corresponding to the item characteristic vectors of which thelevel of similarity satisfies a first condition; common recommendationreason determining means for determining one of the N attributes of theplurality of recommended items determined by the recommending means as acommon recommendation reason when the determined attribute satisfies asecond condition, the common recommendation reason being common to theplurality of recommended items; and presenting means for presenting theuser with either the plurality of recommended items determined by therecommending means or information about the recommended items, togetherwith the common recommendation reason determined by the commonrecommendation reason determining means.

One preferred structure of the first information processing apparatusaccording to the invention may further include item-specificrecommendation reason determining means which establishes successivelyeach of the plurality of recommended items determined by therecommending means as the item of interest, performs a first calculationon each of the N components of the item characteristic vector for theitem of interest by use of a component value corresponding to each ofthe N components as well as the corresponding component valueconstituting part of the comparison basis vector, determines the valueresulting from the first calculation as a recommendation reason level ofthe attribute corresponding to the component being dealt with, anddetermines as an item-specific recommendation reason the attribute ofwhich the recommendation reason level satisfies a third condition;wherein the common recommendation reason determining means may establishsuccessively each of the N attributes as the attribute of interest,perform a second calculation using each of the recommendation reasonlevels determined by the item-specific recommendation reason determiningmeans about the attribute of interest for each of the plurality ofrecommended items, and determine the attribute of interest as the commonrecommendation reason common to the plurality of recommended items ifthe value resulting from the second calculation satisfies the secondcondition.

According to another embodiment of the present invention, there isprovided a first information processing method including the steps of:(a) calculating levels of similarity between a standard form vector as acomparison basis vector and each of a plurality of item characteristicvectors of the standard form which are characteristic of a plurality ofitems, the standard form vector being made up of N componentsindividually representative of N attributes of each of the plurality ofitems, N being an integer of at least 1, step (a) further determining asthe items to be recommended to a user a plurality of items correspondingto the item characteristic vectors of which the level of similaritysatisfies a first condition; (b) determining one of the N attributes ofthe plurality of recommended items determined in step (a) as a commonrecommendation reason common to the plurality of recommended items whenthe determined attribute satisfies a second condition; and (c)controlling presentation to the user of either the plurality ofrecommended items determined in step (a) or information about therecommended items, together with the common recommendation reasondetermined in step (b).

According to a further embodiment of the present invention, there isprovided a first program for causing a computer to carry out a procedureincluding the steps of: (a) calculating levels of similarity between astandard form vector as a comparison basis vector and each of aplurality of item characteristic vectors of the standard form which arecharacteristic of a plurality of items, the standard form vector beingmade up of N components individually representative of N attributes ofeach of the plurality of items, N being an integer of at least 1, step(a) further determining as the items to be recommended to a user aplurality of items corresponding to the item characteristic vectors ofwhich the level of similarity satisfies a first condition; (b)determining one of the N attributes of the plurality of recommendeditems determined in step (a) as a common recommendation reason common tothe plurality of recommended items when the determined attributesatisfies a second condition; and (c) controlling presentation to theuser of either the plurality of recommended items determined in step (a)or information about the recommended items, together with the commonrecommendation reason determined in step (b).

Where the above-outlined first information processing apparatus, firstinformation processing method, and first program of the presentinvention are in use, levels of similarity are first calculated betweena standard form vector as a comparison basis vector and each of aplurality of item characteristic vectors of the standard form which arecharacteristic of a plurality of items. The standard form vector is madeup of N components individually representative of N attributes of eachof the plurality of items, N being an integer of at least 1. A pluralityof items corresponding to the item characteristic vectors of which thelevel of similarity satisfies a first condition are determined as theitems to be recommended to a user. One of the N attributes of theplurality of recommended items determined above is determined as acommon recommendation reason common to the plurality of recommendeditems when the determined attribute satisfies a second condition. Theuser is then presented with either the plurality of recommended itemsdetermined above or information about the recommended items togetherwith the above-determined common recommendation reason.

According to an even further embodiment of the present invention, thereis provided a second information processing apparatus including: commonrecommendation reason determining means for determining in advance oneof N attributes of items as a common recommendation reason common to aplurality of items to be recommended to a user when the determinedattribute satisfies a first condition, N being an integer of at least 1;recommending means which, from among a plurality of item characteristicvectors of a standard form which are characteristic of a plurality ofitems, the standard form being a vector of which N componentsindividually represent the N attributes, selects at least one itemcharacteristic vector of which the component corresponding to theattribute determined as the common recommendation reason by the commonrecommendation reason determining means satisfies a second condition,the recommending means further calculating levels of similarity betweena comparison basis vector of the standard form and each of at least oneitem characteristic vector selected in order to determine, as the itemsto be recommended to a user, a plurality of items corresponding to theitem characteristic vectors of which the level of similarity satisfies athird condition; and presenting means for presenting the user witheither the plurality of recommended items determined by the recommendingmeans or information about the recommended items, together with thecommon recommendation reason determined by the common recommendationreason determining device.

According to a still further embodiment of the present invention, thereis provided a second information processing method including the stepsof: (a) determining in advance one of N attributes of items as a commonrecommendation reason common to a plurality of items to be recommendedto a user when the determined attribute satisfies a first condition, Nbeing an integer of at least 1; (b) from among a plurality of itemcharacteristic vectors of a standard form which are characteristic of aplurality of items, the standard form being a vector of which Ncomponents individually represent the N attributes, selecting at leastone item characteristic vector of which the component corresponding tothe attribute determined as the common recommendation reason in step (a)satisfies a second condition, step (b) further calculating levels ofsimilarity between a comparison basis vector of the standard form andeach of at least one item characteristic vector selected in order todetermine, as the items to be recommended to a user, a plurality ofitems corresponding to the item characteristic vectors of which thelevel of similarity satisfies a third condition; and (c) controllingpresentation to the user of either the plurality of recommended itemsdetermined in step (b) or information about the recommended items,together with the common recommendation reason determined in step (a).

According to a yet further embodiment of the present invention, there isprovided a second program for causing a computer to carry out aprocedure including the steps of: (a) determining in advance one of Nattributes of items as a common recommendation reason common to aplurality of items to be recommended to a user when the determinedattribute satisfies a first condition, N being an integer of at least 1;(b) from among a plurality of item characteristic vectors of a standardform which are characteristic of a plurality of items, the standard formbeing a vector of which N components individually represent the Nattributes, selecting at least one item characteristic vector of whichthe component corresponding to the attribute determined as the commonrecommendation reason in step (a) satisfies a second condition, step (b)further calculating levels of similarity between a comparison basisvector of the standard form and each of at least one item characteristicvector selected in order to determine, as the items to be recommended toa user, a plurality of items corresponding to the item characteristicvectors of which the level of similarity satisfies a third condition;and (c) controlling presentation to the user of either the plurality ofrecommended items determined in step (b) or information about therecommended items, together with the common recommendation reasondetermined in step (a).

Where the above-outlined second information processing apparatus, secondinformation processing method, and second program of the presentinvention are in use, one of N attributes of items is determined inadvance as a common recommendation reason common to a plurality of itemsto be recommended to a user when the determined attribute satisfies afirst condition, N being an integer of at least 1. From among aplurality of item characteristic vectors of a standard form which arecharacteristic of a plurality of items (the standard form is a vector ofwhich N components individually represent the N attributes), at leastone item characteristic vector is selected when the component thereofcorresponding to the attribute determined above as the commonrecommendation reason satisfies a second condition. Levels of similarityare then calculated between a comparison basis vector of the standardform and each of at least one item characteristic vector selected inorder to determine, as the items to be recommended to a user, aplurality of items corresponding to the item characteristic vectors ofwhich the level of similarity satisfies a third condition. The user isthen presented with either the plurality of recommended items determinedabove or information about the recommended items together with theabove-determined common recommendation reason.

As outlined above, the presently preferred embodiments of the presentinvention are capable of presenting the user with recommended itemstogether with reasons for the recommendation. What is noteworthy is thateach user is presented with a convincing reason for the items that havebeen presented to that user.

BRIEF DESCRIPTION OF THE DRAWINGS

Further objects and advantages of the present invention will becomeapparent upon a reading of the following description and appendeddrawings in which:

FIG. 1 is a block diagram showing a typical functional structure of aninformation processing system or apparatus to which the presentinvention is applied;

FIG. 2 is a schematic view showing a typical image presented to the userby the information processing apparatus of FIG. 1;

FIG. 3 is a schematic view showing another typical image presented tothe user by the information processing apparatus of FIG. 1;

FIG. 4 is a schematic view showing another typical image presented tothe user by the information processing apparatus of FIG. 1;

FIG. 5 is a schematic view showing another typical image presented tothe user by the information processing apparatus of FIG. 1;

FIG. 6 is a flowchart of steps constituting a common recommendationreason presenting process carried out by the information processingapparatus of FIG. 1;

FIG. 7 is a tabular view listing levels of similarity between candidatesof recommended items determined in step S1 of FIG. 6 on the one hand,and a user preference vector on the other hand;

FIG. 8 is a typical recommendation list generated in step S1 of FIG. 6;

FIG. 9 is a flowchart of detailed steps constituting the process ofcalculating the recommendation reason level of each item, the processbeing carried out in step S2 of FIG. 6 as part of the commonrecommendation reason presenting process;

FIG. 10 is a tabular view showing examples of a user preference vectorand an item characteristic vector corresponding respectively to the userand each item handled in the process of FIG. 9;

FIG. 11 is a tabular view showing a typical recommendation reason listgenerated as a result of the process of FIG. 9 using the user preferencevector and item characteristic vector given in FIG. 10;

FIG. 12 is a flowchart of detailed steps constituting the process ofcalculating a common recommendation reason level, the process beingcarried out in step S3 of FIG. 6 as part of the common recommendationreason presenting process;

FIG. 13 is a tabular view showing how a common recommendation reason isdetermined by execution of the process in FIG. 12 using therecommendation reason list of FIG. 11;

FIG. 14 is a tabular view showing how a common recommendation reason isdetermined by use of the same recommendation reason list of FIG. 11except that a common recommendation reason determining techniquedifferent from the one applied to the example of FIG. 12 is adopted;

FIG. 15 is a tabular view showing effective vectors as typicalinformation for determining a common recommendation reason where acommon recommendation reason determining technique different from theone applied to the example of FIG. 12 or FIG. 14 is adopted;

FIG. 16 is a flowchart of detailed steps constituting the process ofcalculating a common recommendation reason level, the process beingcarried out in step S3 of FIG. 6 as part of the common recommendationreason presenting process but being different from the example of FIG.12; and

FIG. 17 is a block diagram showing a typical hardware structure of theinformation processing apparatus to which the present invention isapplied.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

What is described below as the preferred embodiments of the presentinvention corresponds to the appended claims as follows: the descriptionof the preferred embodiments basically provides specific examplessupporting what is claimed. If any example of the invention describedbelow as a preferred embodiment does not have an exactly correspondingclaim, this does not means that the example in question has no relevanceto the claims. Conversely, if any example of the invention describedhereunder has a specifically corresponding claim, this does not meanthat the example in question is limited to that claim or has norelevance to other claims.

Furthermore, the description below of the preferred embodiments does notclaim to include all examples corresponding to the whole claims. Inother words, the description hereunder does not limit or deny anyinventive entities which are not covered by the appended claims of thepresent invention but which may be added or brought about by thisapplicant in the future by divisional application or by amendment.

The present invention provides, as outlined above, the first informationprocessing apparatus (e.g., information processing apparatus in FIG. 1)including: a recommending device (e.g., recommendation list generatingdevice 3 in FIG. 1 for carrying out step S1 of FIG. 6) calculatinglevels of similarity between a standard form vector (e.g., vector suchas aroma, color (red, white), and variety (A, B) in FIG. 10 where itemsare wines) as a comparison basis vector (e.g., user preference vectorstored in a user preference vector storing device 1 in FIG. 1; morespecifically, the vector on row 1 in FIG. 10 shown as “user”) and eachof a plurality of item characteristic vectors (e.g., item characteristicvectors stored in an item characteristic vector storing device 2 in FIG.1; more specifically, the item characteristic vectors on lines 2 through4 in FIG. 10 shown as “first item,” “third item” and “fifth item”) ofthe standard form which are characteristic of a plurality of items, thestandard form vector being made up of N components individuallyrepresentative of N attributes of each of the plurality of items, Nbeing an integer of at least 1, the recommending device furtherdetermining as the items to be recommended to a user a plurality ofitems corresponding to the item characteristic vectors of which thelevel of similarity satisfies a first condition; a common recommendationreason determining device (e.g., common recommendation reasondetermining device 5 in FIG. 1 to which are applied diverse techniquescorresponding to the common recommendation reason presenting process instep S3 of FIG. 6) determining one of the N attributes of the pluralityof recommended items determined by the recommending device as a commonrecommendation reason when the determined attribute satisfies a secondcondition, the common recommendation reason being common to theplurality of recommended items; and a presenting device (e.g.,presenting device 6 in FIG. 1 for carrying out steps S4 of FIG. 6)presenting the user with either the plurality of recommended itemsdetermined by the recommending device or information about therecommended items (e.g., appellations such as “Chateau - - -” and“Chateau xxx” in the image 11 of FIG. 2 where items are wines), togetherwith the common recommendation reason (e.g., “wines with a robust aroma”in the image 11 of FIG. 2 where items are wines) determined by thecommon recommendation reason determining device.

One preferred structure of the first information processing apparatusabove may further include an item-specific recommendation reasondetermining device (e.g., recommendation reason determining device 4 inFIG. 1 for performing the process of calculating the commonrecommendation reason level of each item in FIGS. 6 and 9) whichestablishes successively each of the plurality of recommended itemsdetermined by the recommending device as the item of interest, performsa first calculation based on a first calculation technique on each ofthe N components of the item characteristic vector for the item ofinterest by use of a component value corresponding to each of the Ncomponents as well as the corresponding component value constitutingpart of the comparison basis vector, determines the value resulting fromthe first calculation as a recommendation reason level of the attributecorresponding to the component being dealt with, and determines as anitem-specific recommendation reason the attribute of which therecommendation reason level satisfies a third condition; wherein thecommon recommendation reason determining device may establishsuccessively each of the N attributes as the attribute of interest,perform a second calculation based on a second calculation techniqueusing each of the recommendation reason levels determined by theitem-specific recommendation reason determining device about theattribute of interest for each of the plurality of recommended items,and determine the attribute of interest as the common recommendationreason common to the plurality of recommended items if the valueresulting from the second calculation satisfies the second condition(e.g., through execution of the process of calculating a commonrecommendation reason level in FIG. 12).

The present invention also provides the first information processingmethod including the steps of: (a) calculating levels of similaritybetween a standard form vector as a comparison basis vector and each ofa plurality of item characteristic vectors of the standard form whichare characteristic of a plurality of items, the standard form vectorbeing made up of N components individually representative of Nattributes of each of the plurality of items, N being an integer of atleast 1, step (a) further determining as the items to be recommended toa user a plurality of items corresponding to the item characteristicvectors of which the level of similarity satisfies a first condition(e.g., step S1 of FIG. 6); (b) determining one of the N attributes ofthe plurality of recommended items determined in step (a) as a commonrecommendation reason common to the plurality of recommended items whenthe determined attribute satisfies a second condition (e.g., process ofcalculating a common recommendation reason level in step S2 of FIG. 6and in FIG. 12); and (c) controlling presentation to the user of eitherthe plurality of recommended items determined in step (a) or informationabout the recommended items, together with the common recommendationreason determined in step (b)(e.g., step S4 of FIG. 6).

The present invention further provides the second information processingapparatus (e.g., information processing apparatus 1 in FIG. 1)including: a common recommendation reason determining device (e.g.,common recommendation reason determining device 5 in FIG. 5 for carryingout step S51 in FIG. 16) determining in advance one of N attributes ofitems as a common recommendation reason common to a plurality of itemsto be recommended to a user when the determined attribute satisfies afirst condition, N being an integer of at least 1; a recommending device(e.g., recommendation list generating device 3 in FIG. 1 for carryingout steps S52 and S53 in FIG. 16) which, from among a plurality of itemcharacteristic vectors of a standard form which are characteristic of aplurality of items, the standard form being a vector of which Ncomponents individually represent the N attributes, selects at least oneitem characteristic vector of which the component corresponding to theattribute determined as the common recommendation reason by the commonrecommendation reason determining device satisfies a second condition,the recommending device further calculating levels of similarity betweena comparison basis vector of the standard form and each of at least oneitem characteristic vector selected in order to determine, as the itemsto be recommended to a user, a plurality of items corresponding to theitem characteristic vectors of which the level of similarity satisfies athird condition; and a presenting device (e.g., presenting device 6 inFIG. 1 for carrying out step S54 in FIG. 16) presenting the user witheither the plurality of recommended items determined by the recommendingdevice or information about the recommended items, together with thecommon recommendation reason determined by the common recommendationreason determining device.

The present invention also provides the second information processingmethod including the steps of: (a) determining in advance one of Nattributes of items as a common recommendation reason common to aplurality of items to be recommended to a user when the determinedattribute satisfies a first condition, N being an integer of at least 1(e.g., step S51 in FIG. 16); (b) from among a plurality of itemcharacteristic vectors of a standard form which are characteristic of aplurality of items, the standard form being a vector of which Ncomponents individually represent the N attributes, selecting at leastone item characteristic vector of which the component corresponding tothe attribute determined as the common recommendation reason in step (a)satisfies a second condition, step (b) further calculating levels ofsimilarity between a comparison basis vector of the standard form andeach of at least one item characteristic vector selected in order todetermine, as the items to be recommended to a user, a plurality ofitems corresponding to the item characteristic vectors of which thelevel of similarity satisfies a third condition (e.g., steps S52 and S53in FIG. 16); and (c) controlling presentation to the user of either theplurality of recommended items determined in step (b) or informationabout the recommended items, together with the common recommendationreason determined in step (a)(e.g., step S54 in FIG. 16).

The present invention further provides the program corresponding to theabove-outlined first or second information processing method, as well asa recording medium which accommodates that program. The program isperformed illustratively by a computer outlined in FIG. 17, as will bediscussed later.

In preparation for a detailed description of the preferred embodimentsof the present invention, some words and phrases are specificallydefined and some basic techniques are explained below.

In this specification, “items” are defined as any software or hardware(i.e., products) that may be offered to users. For example, pieces ofsoftware referred to as contents such as TV programs, movies, photos andtunes (i.e., moving pictures, still pictures, voices, and theircombinations) are some of the items applicable to this specification.Products such as wines (hardware) are also items that fall within thescope of this specification, and so are sentences and evenconversations.

For purpose of simplification and illustration, wines put in suitablecontainers (e.g., 750-ml bottles) will be adopted as typical items inthe description that follows.

Outlined below is a typical series of steps carried out generally forrecommending items (the steps are simply called the recommendationprocess hereunder). It is assumed for convenience of explanation thatone information processing apparatus performs the entire recommendationprocess.

A typical information processing apparatus first turns an item into avector using as a basis vector each of N pieces of information (N is aninteger of 1 or larger) denoting the characteristics of the item inquestion. In this case, the information processing apparatus weights asneeded each of the components constituting the vector through the use ofa suitable weighting technique.

In the description that follows, the vector of the item thus processedwill be called the item characteristic vector. The information denotingthe characteristics of the item will be referred to as attributeinformation, and pieces of information constituting attributeinformation as attributes. If a given item is associated with theattribute information about each of N attributes, then the N pieces ofattribute information are expressed in numerical form (i.e., weighted).The N numeric values derived from these numbers are regarded as thecomponents constituting an item characteristic vector.

For example, if the items handled are wines, their attributes includeprices, varieties of the grapes used, taste, and aroma. Suppose that agiven wine is priced at ¥10,000, made from the variety “a,” andevaluated at 3 on the taste scale of 5 and at 2 on the aroma scale of 5.In that case, the wine is associated with information denoting theattributes “¥10,000,” “variety a,” “3,” and “2.” If the itemcharacteristic vector of the wine is assumed to be defined by thecomponent vectors of “price, grape variety, taste, and aroma,” then theattributes “ø10,000,” “variety a,” “3,” and “2” are turned into numericvalues α, β, γ, and θ (α, β, γ, and θ are any values independent of oneanother). These values are substituted into the first through the fourthcomponents of a vector that forms the item characteristic vector for thewine in question.

If any of the N pieces of attribute information is not associated with agiven item, zero is substituted into the component representing thecorresponding attribute as part of the item characteristic vector ofthat item.

A standard form vector is defined as a vector whose N components arearrayed in their predetermined sequence. In the foregoing example, thevector having the components “price, grape variety, taste, aroma”arrayed in that order is regarded as a standard form vector with respectto the item characteristic vectors of wines.

Traditionally, the information processing apparatus generates and storesthe item characteristic vectors of that standard form for each of theitems involved.

Meanwhile, typical information processing apparatuses traditionallygenerate a standard form vector indicative of each user's preferences byresorting to the user's history and by utilizing various kinds ofinformation input by the user in question. In the description thatfollows, the standard form vector representative of each user'spreferences will be called the user preference vector (UPV).

Typically, the information processing apparatus finds levels ofsimilarity between the user preference vector of each user on the onehand, and a plurality of stored item characteristic vectors in terms ofcosine correlation and other factors on the other hand (i.e., in amatching process). The items associated with the item characteristicvectors whose levels of similarity are higher than a threshold value arepresented to the user in question as recommended items. The abovedescription has shown how traditional recommendation systems typicallywork.

As mentioned above, there already exist some information processingapparatuses that present to the user both recommended items and thereason for the recommendation. However, when presenting a plurality ofrecommended items to the user, the information processing apparatus hastypically determined an independent reason for recommending each of themultiple recommended items. The technique of determining such reasons isrelatively simple: of the components (attributes) of the standard formvector, the one (attribute) having the highest level of similaritybetween the UPV and the relevant item characteristic vector is presentedas the reason for recommendation.

It should be noted that in the description that follows, therecommendation reason determined individually for each of a plurality ofrecommended items will be referred to as an item-specific recommendationreason.

Under the above circumstances, a large number of users presented withsuch item-specific recommendation reasons have failed to understand whysuch and such items were recommended. That is, information processingapparatuses have typically failed to present users with convincingreasons for recommendation.

With a view to overcoming such disadvantages and other drawbacks of therelated art, this applicant has come up with the following technique:when a plurality of recommended items are to be presented, they arearranged into groups each sharing a minimum of one common attributecharacterizing the group in question. The common attribute or attributesare regarded as a theme representative of the recommended item group asa whole (i.e., made up of a plurality of recommended items), and thattheme is presented to the user. The above technique is implemented inspecific steps proposed by this applicant. The theme is the reason forall that is recommended to the user (to whom a plurality of items arerecommended). The theme will be called the common recommendation reasonin the description that follows.

Adopting the inventive technique above makes it possible to present theuser with recommended items together with convincing reasons. To be morepersuasive, the common recommendation reason presented to the user maybe accompanied by an item-specific recommendation reason for each of therecommended items. Detailed steps for determining the commonrecommendation reason will be discussed later by referring FIG. 6 andsubsequent drawings.

A preferred embodiment of the present invention will now be describedwith reference to the accompanying drawings.

FIG. 1 is a block diagram showing a typical functional structure of arecommendation system to which the above-outlined inventive technique isapplied and which embodies the present invention. In operation, therecommendation system of FIG. 1 searches a plurality of items for theitems to be recommended to the user, determines a common recommendationreason (i.e., theme) for the recommended items, and presents the userwith the plurality of recommended items together with the theme.

In this specification, the term “system” refers to an entireconfiguration made up of a plurality of component devices and aprocessor or processors. In this context, the recommendation system ofFIG. 1 may be constituted by a single information processing apparatusor by a plurality of such apparatuses. For the presently preferredembodiment, it is assumed that the recommendation system of FIG. 1 ismade up of one information processing apparatus. That is, FIG. 1illustrates a typical functional structure of the information processingapparatus to which the present invention is applied.

In the example of FIG. 1, the information processing apparatus isarranged to include diverse component devices ranging from a userpreference vector storing device 1 to a presenting device 6.

When implemented, each of the component devices ranging from the userpreference vector storing device 1 to the presenting device 6 is notlimited in any way. In particular, a recommendation list generatingdevice 3, a recommendation reason determining device 4, and a commonrecommendation reason determining device 5 may each be structured bysoftware, by hardware or by a combination of both.

The user preference vector storing device 1 holds standard form userpreference vectors. An item characteristic vector storing device 2 holdsa standard form item characteristic vector for each of a plurality ofitems. With this embodiment, for example, “n” items (n is an integer ofat least 1) are associated with “n” item characteristic vectors storedin the item characteristic vector storing device 2.

More specifically, this embodiment assumes that the items handled arewines and that standard form user preference vectors and itemcharacteristic vectors are each formed by the components of “aroma,color (red), color (white), variety A, and variety B.” Each of thecomponents will be discussed later in more detail.

The recommendation list generating device 3 finds levels of similaritybetween the user preference vector held in the user preference vectorstoring device 1 on the one hand, and “n” item characteristic vectorsheld in the item characteristic vector storing device 2 in terms ofcosine correlation and other factors on the other hand. Based on thelevels of similarity thus obtained, the recommendation list generatingdevice 3 calculates the recommendation reason level for each of theitems corresponding to the plurality of item characteristic vectors.Although the technique of calculating recommendation reason levels isnot subject to particular constraints, this embodiment considers thelevel of similarity with regard to be the recommendation reason levelfor purpose of illustration and simplification. The recommendation listgenerating device 3 proceeds to determine as the items to be recommendedto the user at least one item (i.e., wines for this embodiment)corresponding to the item characteristic vectors whose recommendationreason levels are higher than a predetermined threshold value. Therecommendation list generating device 3 then generates a list thatprovides information identifying the recommended item or items (the listis called the recommendation list), and forwards the generated list tothe recommendation reason determining device 4.

The information identifying recommended items such as wines for thisembodiment illustratively includes the appellations of wines, theirproducing regions, and winemakers (domaines and wineries). Naturally,the recommendation list may also be arranged to include otherinformation such as the prices of wines and the reasons for recommendingparticular wines. With this embodiment, however, the recommendation listoutput by the recommendation list generating device 3 includes only thenames of the items (i.e., appellations of wines) and the recommendationreasons (i.e., levels of similarity with this embodiment) associatedtherewith, as will be described later with reference to FIG. 8.

The recommendation reason determining device 4 determines anitem-specific recommendation reason for each of the recommended item oritems contained in the recommendation list supplied by therecommendation list generating device 3, generating a list thatenumerates the results of what has been determined (see FIG. 11, to bediscussed later). The list thus generated is sent to the commonrecommendation reason determining device 5 together with therecommendation list. That list is called a recommendation reason list asdistinguished from the recommendation list and other lists in thedescription that follows. The technique of generating the recommendationreason list will be discussed later along with specific examples of thelist.

Based on the recommendation reason list coming from the recommendationreason determining device 4, the common recommendation reasondetermining device 5 determines a common recommendation reason (i.e.,theme). The common recommendation reason thus determined is forwarded tothe presenting device 6 together with the recommendation list andrecommendation reason list mentioned above.

Given the common recommendation reason (theme), recommendation list, andrecommendation reason list from the common recommendation reasondetermining device 5, the presenting device 6 generates an image whichat least includes the common recommendation reason and each of therecommended items (and their names). The generated image, called arecommendation image, is then presented to the user. That is, thepresenting device 6 of this embodiment is structured to have a suitabledisplay unit that displays the recommendation image.

The recommendation image may be any kind of image as long as it includesthe common recommendation reason and each of the recommended items andtheir names as mentioned above.

More specifically, the images shown in FIGS. 2 through 5 may be adoptedas recommendation images. These images are examples of what is presentedto the user in image form.

In the example of FIG. 2, a recommendation image 11 displays messages“welcome to the wine recommending service” and “the following wines arerecommended to you favoring - - -,” in that order from the top down. Inthe second message, a common recommendation reason (theme) is placed inthe blank field. In FIG. 2, the common recommendation reason (theme) of“wines with a robust aroma” is indicated. The user can easily recognizethat the wines listed in this recommendation image 11 are recommendedprimarily for their robust aroma.

Under the indication of the common recommendation reason is theinformation identifying the specific items recommended. In the exampleof FIG. 2, each row of the information indicates the appellation of arecommended wine on the left-hand side, and the level of similarity withregard to the user preference vector in effect on the right-hand side.In FIG. 2, each level of similarity is represented by the number ofsolid stars (*). The larger the number of solid stars, the higher thelevel of similarity. More specifically, in FIG. 2, the wine with theappellation “chateau ◯◯◯” is recommended with the stars representinglevel five of similarity, and the wine with the appellation “chateauXXX” is recommended with the stars denoting level three of similarity.

For most of the recommended items, the attribute adopted as the commonrecommendation reason (“aroma” in the example of FIG. 2) is alsoemployed as the item-specific recommendation reason for each item. Forsome recommended items, however, the attribute selected as the commonrecommendation reason is not used as an item-specific recommendationreason.

In such cases, the recommended items for which the attribute adopted asthe common recommendation reason is not used as the item-specificrecommendation reason may be excluded (the excluded items are called“commonly recommended but individually unadvised items”) from therecommendation image (i.e., not presented to the user). Alternatively,these items may be considered “unexpected” but recommendable items forthe user and may be included in the recommendation image (i.e.,presented to the user) along with an appropriate comment. For example, arecommendation image 12 shown in FIG. 3 may be used to give suchpresentations.

The recommendation image 12 in FIG. 3 is basically the same in layout asthe above-described recommendation image 11 in FIG. 2 and thus will notbe described further.

In the example of FIG. 3, the wine with the appellation “ΔΔ(domaine □□)is presented as a commonly recommended but individually unadvised item.The aroma adopted as the common recommendation reason does not apply tothis wine as an item. Thus on the right-hand side of the star notationfor this item appears a comment “with a twist in taste” indicating thatthis recommended item is a surprise for the user.

On the other hand, the wines with the appellations “chateau ◯◯◯” and“chateau XXX” each have an item-specific recommendation reason “aroma”that is identical to their common recommendation reason. For thatreason, no comment is furnished to the right of their star notations.

As another alternative, images 13-1 through 13-3 shown in FIG. 4 may beadopted as recommendation images. The example in FIG. 4 presupposes theuse of a small-size screen such as the screen of portable terminals. Theimages are divided so that each of them may be accommodated by theundersized screen. Each of the images 13-1 through 13-3 carries amessage indicating the page number (pages 1, 2, 3). The image 13-1, thefirst of the series of pages, includes at its bottom an operation areaindicated as “look for more.” This area is operated on to look up thenext page (image 13-2). The operation area is also furnished on thesecond page (image 13-2).

Obviously, the number of divided recommendation images is not limited tothat of the example in FIG. 4 (with three images). In general, thenumber of divided recommendation images is dependent on the screen sizein use and on the total number of recommended items.

What distinguishes the example of FIG. 4 from that of FIG. 2 or 3 isthat in each of the recommended images 13-1 through 13-3 in FIG. 4, acircle (◯) or a cross (X) is indicated in the rightmost position of eachof the rows denoting the recommended items.

The circle symbol (◯) shows that the item in question is a recommendeditem for which the common recommendation reason (“aroma” in the exampleof FIG. 4) is also adopted as the item-specific recommendation reason(the item is called a commonly and individually recommended item). Thecross symbol (X) indicates that the item is a commonly recommended butindividually unadvised item.

The recommendation images 13-1 through 13-3 in FIG. 4 are basically thesame in terms of layout as the above-described recommendation image 11in FIG. 2 or recommendation image 13 in FIG. 3 and thus will not bedescribed further.

In any recommendation image, the sequence of a plurality of recommendeditems displayed is not limited. That is, the recommended items may bedisplayed in any sequence, each of their ordinal positions beingindicated numerically in the leftmost position of each row.

More specifically, the recommendation images 13-1 through 13-3 in FIG. 4show illustratively the recommended items in order of the levels ofsimilarity with regard to the user reference vector in effect (i.e., inorder of recommendation levels). Alternatively, the recommended itemsmay be arrayed in such a sequence as is indicated in recommendationimages 14-1 through 14-3 of FIG. 5.

The recommendation images 14-1 through 14-3 in FIG. 5 have therecommended items, which are the same as those in the recommendationimages 13-1 through 13-3 of FIG. 4, arranged in the following sequence:the commonly and individually recommended items (furnished with thecircle symbol (◯)) come first, followed by the commonly recommended butindividually unadvised items (with the cross symbol (X)).

Although the circle-furnished recommended items in FIG. 5 are arrayed inorder of the levels of similarity with regard to the user preferencevector in effect (i.e., in order of recommendation levels), this is notlimitative of the invention. Alternatively, the recommended items may bearranged in order of recommendation reason levels (to be describedlater) for the attribute adopted as the common recommendation reason(“aroma” in the examples of FIGS. 4 and 5). As another alternative, therecommended items may be arrayed in descending order of the componentvalues of the attribute adopted as the common recommendation reason(“aroma” in the examples of FIGS. 4 and 5).

Likewise, whereas the cross-furnished recommended items in FIG. 5 arearrayed in order of the levels of similarity with regard to the userpreference vector in effect (i.e., in order of recommendation levels),this is not limitative of the invention.

Some examples of the recommendation images have been described abovewith reference to FIGS. 2 through 5. To reiterate the foregoingexplanation, the implementation of recommendation images is not limitedto the examples in FIGS. 2 through 5. Illustratively, the levels ofsimilarity shown on the right-hand side of each recommended item (i.e.,recommended wines) in the examples of FIGS. 2 through 5 may besupplemented with diverse kinds of information (not shown) including theitem-specific recommendation reason for each item.

Described below with reference to the flowchart of FIG. 6 is a series ofsteps performed by the information processing apparatus of FIG. 1 inorder to present the user with recommended items and a commonrecommendation reason (theme) for these items. The series of stepsreferred to as the common recommendation reason presenting process inFIG. 6 will also be mentioned as such in the description that follows.

In step S1, the recommendation list generating device 3 in FIG. 1generates a recommendation list and forwards the generated list to therecommendation reason determining device 4.

In a first stage of step S1, as described above, the recommendation listgenerating device 3 calculates the levels of similarity between the userpreference vector of a given user held in the user preference vectorstoring device 1 on the one hand, and each of the characteristic vectorsfor each of a first through an n-th item stored in the itemcharacteristic vector storing device 2 on the other hand. Typical levelsof similarity resulting from the calculation are listed in FIG. 7.

In a second stage of step S1, the recommendation list generating device3 determines recommendation levels of the items based on the calculatedlevels of similarity, and selects as the recommended items those itemsof which the recommendation levels correspond to the item characteristicvectors satisfying a predetermined condition. With this embodiment, thelevels of similarity are taken unmodified as the recommendation levelsas mentioned earlier. It is assumed for this embodiment that the itemswhose levels of similarity (recommendation levels) are 0.6 or higher aredetermined as the recommended items. On that assumption, as indicated inFIG. 7, the first, third and the fifth items with their levels ofsimilarity at 0.8, 0.9 and 0.7 respectively are determined as therecommended items.

In a third stage of step S1, the recommendation list generating device 3generates a recommendation list that includes the recommended item oritems (i.e., appellations) determined in the second stage and the levelsof similarity of these items, and supplies the generated list to therecommendation reason determining device 4. Illustratively, arecommendation list 21 shown in FIG. 8 is generated (i.e., a typicalrecommendation list).

After the recommendation list is generated by the recommendation listgenerating device 3 and forwarded to the recommendation reasondetermining device 4, control is passed on to step S2.

In step S2, as mentioned above, the recommendation reason determiningdevice 4 determines an item-specific recommendation reason for each ofthe recommended item or items included in the recommendation list, andgenerates a recommendation reason list that enumerates the determinedreasons. The recommendation reason list is sent to the commonrecommendation reason determining device 5 together with therecommendation list. In the ensuing description, what takes place instep S2 will be referred to as the process of calculating therecommendation reason level of each item. That process will be discussedlater in more detail with reference to FIGS. 9 through 11.

In step S3, as described above, the common recommendation reasondetermining device 5 determines a common recommendation reason (theme)based on the recommendation reason list supplied from the recommendationreason determining device 4. The common recommendation reason thusdetermined is supplied to the presenting device 6 together with therecommendation list and recommendation reason list. In the ensuingdescription, what takes place in step S3 will be referred to as theprocess of calculating a common recommendation reason level. Thatprocess will be discussed later in more detail with reference to FIGS.12 through 15.

In step S4, the presenting device 6 presents the user with therecommended items along with recommended points (i.e., item-specificrecommendation reasons) and the theme common to all recommended items(i.e., common recommendation reason). This completes the commonrecommendation reason presenting process.

In the common recommendation reason presenting process of FIG. 6,recommended points are shown presented in step S4. However, thepresentation of recommended points is not mandatory. Whereas the exampleof FIG. 6 is shown presenting item-specific recommendation reasons asthe recommended points, this is not limitative of the invention.Alternatively, levels of similarity may be presented in their place asdescribed above. In this case, if what is presented is assumed to appearas images on the screen, then recommendation images such as those inFIGS. 2 through 5 above are presented to the user in step S4.

Given below are detailed descriptions of the process of calculating therecommendation reason level of each item in step S2, and of the processof calculating a common recommendation reason level in step S3. Theprocesses in steps S2 and S3 will be discussed separately in detail, inthat order.

First to be detailed with reference to FIGS. 9 through 11 is the processof calculating the recommendation reason level of each item in step S2.FIG. 9 is a flowchart of detailed steps constituting that process.

In step S11 of FIG. 9, the recommendation reason determining device 4acquires the user preference vector of a given user from the userpreference vector storing device 1.

Illustratively, it is assumed that the vector on row 1 named “user” inFIG. 1 is acquired in step S11 as the user preference vector composed of“aroma, color (red), color (white), variety (A), variety (B)=(2.5, 0, 1,0.5, 0.7).” FIG. 1 shows examples of a typical user preference vectorand item characteristic vectors. More about the item characteristicvectors will be discussed later.

Each of the components making up the user preference vector representsthe preferences of the user in question, the preferences being derivedillustratively from the user's past operation history. With thisembodiment, it is assumed for purpose of simplification that the itemcharacteristic vectors of the wines purchased by each user in the pastare stored as operation histories and that the values of the componentsconstituting each user preference vector are determined on the basis ofthe components of the stored item characteristic vectors.

Also with this embodiment, it is assumed for purpose of illustrationthat all wines, regardless of whether they have ever been purchased byany user, have their “aroma,” “variety A,” and “variety B” numericallydetermined on the scale of 0 to 3 by winemakers and/or service providersof the recommendation system in FIG. 1 through sampling. The numbersthus determined are substituted into the first, the fourth, and thefifth components (i.e., aroma, variety A, and variety B) of each itemcharacteristic vector. For the component “aroma,” it is assumed that thehigher the value, the stronger the aroma of the wine in question. Foreach of the varieties A and B, the higher the value, the betterexpressed the characteristic of the variety in question.

If the wine of interest is a red wine, it is assumed that the value of 1is substituted into the second component “color (red)” and zero into thethird component “color (white).” In the case of a white wine, the valueof 0 is substituted into the second component “color (red)” and 1 intothe third component “color (white).” For purpose of simplification,wines that are difficult to classify (rose wine, sparkling wine, etc.)are excluded from the examples.

In the description that follows, what is referred to as the numericattribute in FIG. 10 is an attribute which has only one component suchas the “aroma” in FIG. 10 and of which the level is numerically denoted.Also in the ensuing description, what is referred to as the appellationattribute in FIG. 10 is an attribute which has a plurality of attributes(appellations) such as the “color” group having the components “red” and“white” and the “variety” group made up of the components “A” and “B” asindicated in FIG. 10, the attributes or their components beingrepresented individually by a value or by a true-false notation (◯X).

With this embodiment, as mentioned above, the values of the componentsmaking up each user preference vector are determined on the basis of thecomponents constituting the item characteristic vectors of all winespurchased so far by the user in question. With this embodiment, forpurpose of simplification, the component values of each user preferencevector are determined illustratively as follows:

For each of the user preference vectors of this embodiment, thecomponent values of “aroma,” “variety A,” and “variety B” are eachassumed to be an average of the values of the corresponding component inthe item characteristic vectors of all wines purchased so far by a givenuser.

The values representing each of the components “color (red)” and “color(white)” in the item characteristic vectors of all wines purchased bythe user in the past are totaled. The two sum totals are compared. Thelarger of the two sums indicates the type of wine (red or white) ofwhich the user has so far purchased more wines than the other type. Thevalue of 1 is then substituted into that component of the userpreference vector which denotes the red or the white type preferred bythe user. The value of zero is substituted into that component of theuser preference vector which is representative of the type of wine lessfavored by the user.

Obviously, the technique of determining the value of each of thecomponents making up the user preference vector is not limited to theexamples described above.

Returning to FIG. 9, control is transferred from step S11 to step S12once the user preference vector is acquired.

In step S12, the recommendation reason determining device 4 establishesone of the items included in the recommendation list as the item ofinterest.

In this example, the recommendation list 21 in FIG. 8 has been fed fromthe recommendation list generating device 3 to the recommendation reasondetermining device 4 as described above. Thus in step S12, one of thefirst, the third, and the fifth items is established as the item ofinterest. For this example, it is assumed that the first item isestablished as the item of interest.

In step S13, the recommendation reason determining device 4 acquires theitem characteristic vector of the item of interest from the itemcharacteristic vector storing device 2. In this example, the itemcharacteristic vector of the first item is acquired.

In step S14, the recommendation reason determining device 4 selects theattribute corresponding to one of the components constituting the itemcharacteristic vector. In this example, one of the five attributes“aroma,” “color (red),” “color (white),” “variety (A),” and “variety(B)” is selected in step S14.

In step S15, the recommendation reason determining device 4 checks todetermine whether or not to have the selected attribute included in anitem-specific recommendation reason.

The condition serving as the criterion by which to determine whether tohave the selected attribute included in the item-specific recommendationreason is not limited in any way. Diverse conditions may be adopted withregard to different attributes. Some of the typical conditions will bediscussed later.

If it is determined in step S15 that the selected attribute is not to beincluded in the item-specific recommendation reason, then control ispassed on to step S17.

On the other hand, if it is determined in step S15 that the selectedattribute is to be included in the item-specific recommendation reason,then control is transferred to step S16.

In step S16, the recommendation reason determining device 4 calculatesthe recommendation reason level for the selected attribute of the itemof interest.

The technique of calculating the recommendation reason level is notparticularly limited. Various techniques may be applied to differentattributes. A typical technique of calculating the recommendation reasonlevel will be discussed later.

If the result of the check in step 15 is affirmative (“YES”), then stepS16 is reached and executed followed by step S17. If the result of thecheck in step S15 is negative (“NO”), then step S17 is reachedimmediately.

In step S17, the recommendation reason determining device 4 checks todetermine whether the recommendation reason levels for all attributes ofthe item of interest have been calculated.

If it is determined in step S17 that the recommendation reason levelsfor all attributes have yet to be calculated, then control is returnedto step S14 and the subsequent steps are repeated. In this example, aloop made up of steps S14 through S16 is repeated, whereby some of thefive attributes “aroma,” “color (red),” “color (white),” “variety (A),”and “variety (B)” are selected as item-specific recommendation reasons,and their recommendation reason levels are calculated.

When it is determined in step S17 that the recommendation reason levelsfor all attributes of the items of interest have been calculated,control is passed on to step S18.

In step S18, the recommendation reason determining device 4 checks todetermine whether all items included in the recommendation list havebeen established as the item of interest.

If it is determined in step S18 that not all items in the recommendationlist have been established as the item of interest, then control isreturned to step S12 and the subsequent steps are repeated. In thisexample, a loop formed by steps S12 through S18 is repeated so that eachof the remaining items (i.e., the third and the fifth items in thiscase) is successively established as the item of interest. For each itemof interest thus established, some of the five attributes “aroma,”“color (red),” “color (white),” “variety (A),” and “variety (B)” areselected as item-specific recommendation reasons and theirrecommendation reason levels are calculated.

When it is determined in step S18 that all items included in therecommendation list have been established as the item of interest,control is passed on to step S19.

In step S19, the recommendation reason determining device 4 generates arecommendation reason list. Specifically, it is assumed for this examplethat a recommendation reason list 22 shown in FIG. 11 is generated(i.e., a typical recommendation reason list).

What follows is a description of some typical conditions according towhich this embodiment generates the recommendation reason list 22 ofFIG. 11 in step S19 above, as well as some typical techniques by whichthis embodiment calculates the recommendation reason level in step S16.

It is assumed here that the item characteristic vector of the first itemis the vector named “first item” on row 2 in FIG. 10, the components ofthe vector being set to (1.0, 0, 1, 2.1, 1.0) for “aroma,” “color(red),” “color (white),” “variety (A),” and “variety (B), respectively;that the item characteristic vector of the third item is the vectornamed “third item” on row 3 in FIG. 10, the components of the vectorbeing set to (2.0, 1, 0, 3.0, 2.0) for “aroma,” “color (red),” “color(white),” “variety (A),” and “variety (B), respectively; and that theitem characteristic vector of the fifth item is the vector named “fifthitem” on row 4 in FIG. 10, the components of the vector being set to(3.0, 1, 0, 1.5, 0.5) for “aroma,” “color (red),” “color (white),”“variety (A),” and “variety (B),” respectively.

First to be described is a typical condition serving as the basis forthe check in step S15 regarding the attribute “aroma,” along with atypical technique of calculating the recommendation reason level in stepS16.

In this example, the recommendation reason determining device 4 performsthe expression (1) below to calculate the level of similarity regardingthe attribute “aroma” of the item of interest in step S15. Theexpression is given as follows:

$\begin{matrix}{\begin{matrix}{{level}\mspace{14mu}{of}\mspace{14mu}{similarity}} \\{{{regarding}\mspace{14mu}{attribute}}\mspace{14mu}} \\{{''}{{aroma}{''}}}\end{matrix} = \frac{{Max}_{dist} - {{{Usr}_{value} - {Item}_{value}}}}{{Max}_{dist}}} & (1)\end{matrix}$where,Max_(dist)=Max_(value)−Min_(value)  (2)

In the expression (1) above, Max_(dist) is the value defined by anotherexpression (2) above. In the expression (2), Max_(value) stands for thelargest of the values representing the component “aroma” for the itemcharacteristic vectors of all recommended items in the recommendationlist, and Min_(value) indicates the smallest of these component values.Usr_(value) denotes the value of the component “aroma” of the userpreference vector, and Item_(value) represents the value of thecomponent “aroma” in the item characteristic vector of the item ofinterest.

According to what is shown in FIG. 10, Max_(value) is 3.0 andMin_(value) is 1.0, so that Max_(dist) is 2.0 while Usr_(value) is 2.5.When the first, the third, and the fifth items are each establishedsuccessively as the item of interest, Item_(value) is given as 1.0, 2.0and 3.0, respectively. That means the levels of similarity regarding“aroma” of the first, the third, and the fifth items are given as 0.25,0.75, and 0.75, respectively.

In step S15, the recommendation reason determining device 4 proceeds toadopt (i.e., include) the attribute “aroma” as the item-specificrecommendation reason if the level of similarity regarding “aroma” ofthe item of interest is found higher than a predetermined thresholdvalue. If the level of similarity regarding “aroma” of the item ofinterest is lower than the threshold value, then the attribute “aroma”is excluded from the item-specific recommendation reason.

Suppose that the threshold value is set to 0.7. In that case, theattribute “aroma” of the first item in the above example is excludedfrom the item-specific recommendation reason. For the third and thefifth items, by contrast, the attribute “aroma” is adopted as theitem-specific recommendation reason.

That is, step S16 is not performed on the first item. Step S16 iscarried out on the third and the fifth items so that the recommendationreason level regarding the attribute “aroma” is calculatedillustratively as outlined below.

In step S16, the recommendation reason determining device 4 performs theexpression (3) below to calculate the recommendation reason levelregarding “aroma” of the item of interest. The expression (3) is givenas follows:

$\begin{matrix}{\begin{matrix}{{{recommendation}\mspace{14mu}{reason}}\mspace{11mu}} \\{{{level}\mspace{14mu}{r{egarding}}}\mspace{14mu}} \\{{{attribute}\mspace{14mu}{''}}{{aroma}{''}}}\end{matrix} = \frac{{Max}_{dist} - {{{Item}_{value} - {Discrete}_{value}}}}{{Max}_{dist}}} & (3)\end{matrix}$where,Discrete_(value)={1 or 2 or 3 (in the case of attribute “aroma”)}  (4)

In the expression (3) above, Max_(dist) is the value defined by theexpression (2) shown earlier, and Item_(value) represents the value ofthe component “aroma” in the item characteristic vector of the item ofinterest. In the expression (3) above, each of a predetermined pluralityof discrete values that fall within the probable range of values for“aroma” is substituted into Discrete_(value). In this example, as shownin the expression (4), above, the value of 1, 2 or 3 is substituted intoDiscrete_(value).

In this example, as mentioned above, Max_(dist) is 2.0. When the thirdand the fifth items are each established successively as the item ofinterest, Item_(value) takes on the values of 2.0 and 3.0 respectively.Thus as shown in the recommendation reason list 22 of FIG. 11, the thirditem is given the recommendation reason levels of 0.5, 1.0, and 0.5 atlevels “1,” “2,” and “3” for the attribute “aroma,” respectively.

For the third item, the attribute “aroma” has been adopted as theitem-specific recommendation reason. Each recommendation reason levelindicates which of the three recommendation reason levels “1,” “2,” and“3” for the attribute “aroma” is emphasized, i.e., how much theattribute “aroma” is appreciated when evaluated.

The recommendation reason level of each attribute is used to determine acommon recommendation reason, as will be discussed later. For thatreason, the scale on which to evaluate recommendation reason levelsshould be common to all attributes. With this embodiment, therecommendation reason levels of all attributes are normalized so as tofall within the range of 0 through 1.

Likewise, as shown in the recommendation reason list 22 of FIG. 11, thefifth item is given the recommendation reason levels of 0, 0.5, and 1 atlevels “1,” “2,” and “3” for the attribute “aroma,” respectively.

As described above, the attribute adopted in step S15 as theitem-specific recommendation reason is subjected in step S16 to thecalculation of its recommendation reason level. In step S19, asindicated in FIG. 11, the calculated recommendation reason levels arewritten to the corresponding entries in the recommendation reason list22.

What has been discussed above are the typical condition serving as thebasis for determination in step S15 regarding the attribute “aroma” andthe typical technique of calculating recommendation reason levels forthe same attribute in step S16.

What follows is a description of a typical condition serving as thebasis for determination in step S15 regarding the attribute “color (red,white)” and a typical technique of calculating recommendation reasonlevels for the same attribute in step S16.

In this example, the recommendation reason determining device 4 in stepS15 establishes as the component of interest one of the two components“color (red)” and “color (white),” whichever has the value “1”substituted therein, of the user preference vector. Where the componentof interest in the user preference vector has the value “1” substitutedtherein, the attribute corresponding to the component of interest (e.g.,a particular color) is adopted (i.e., included) as the item-specificrecommendation reason. Where the component of interest has the value “0”substituted therein, the attribute corresponding to that component isexcluded from the item-specific recommendation reason.

More specifically, in the example of FIG. 10, the attribute “color(white)” is established as the component of interest. Thus for the firstitem corresponding to the item characteristic vector in which thecomponent of interest has the value “1” substituted therein, theattribute “color (white)” is adopted as the item-specific recommendationreason. For the third and the fifth items, by contrast, the attributes“color (white)” and “color (red)” are not adopted (i.e., excluded) asthe item-specific recommendation reason.

It follows that step S16 is not performed on the third and the fifthitems while step S16 is carried out on the first item. Therecommendation reason level regarding the attribute “color (white)” forthe first item is calculated illustratively as follows:

In step S16, the recommendation reason determining device 4 takes as therecommendation reason level the component value “1” for the attribute“color (white)” in the item characteristic vector of the item ofinterest. Since the component value is either “0” or “1” for theattributes “color (white)” and “color (red),” there is no need tonormalize the component values of these attributes.

Later in step S19, as shown in FIG. 11, the value “1” is substitutedinto the entry on row 1 (indicating the first item), column 5(indicating the attribute “color (white)”) in the recommendation list22.

What has been discussed above are the typical condition serving as thebasis for determination in step S15 regarding the attribute “color (red,white)” and the typical technique of calculating recommendation reasonlevels for the same attribute in step S16.

What follows is a description of a typical condition serving as thebasis for determination in step S15 regarding the attribute “variety (A,B)” and a typical technique of calculating recommendation reason levelsfor the same attribute in step S16.

In this example, the recommendation reason determining device 4 in stepS15 multiplies each of the values representing the components “variety(A)” and “variety (B)” in the user preference vector by thecorresponding one of the values denoting the components “variety (A)”and “variety (B)” in the item characteristic vector of the item ofinterest. The attribute of which the product is found equal to or higherthan a predetermined threshold value is adopted (i.e., included) as theitem-specific recommendation reason; the attribute of which the productis found lower than the threshold value is excluded from theitem-specific recommendation reason.

More specifically, suppose that the threshold value is defined as 1.0.For the first item in the example of FIG. 10, the product resulting frommultiplication of the values for the component “variety (A)” is 1.05(=0.5×2.1), which is the only product higher than 1.0. Thus theattribute “variety (A)” is adopted as the item-specific recommendationreason for the first item. For the third item, the product frommultiplication of the values for the component “variety (A)” is 1.5(=0.5×3.0) and the product from multiplication of the values for thecomponent “variety (B)” is 1.0 (=0.5×2.0). Because both products areequal to or higher than the threshold value, the attribute “variety (A)”and the attribute “variety (B)” are both adopted as the item-specificrecommendation reason for the third item. For the fifth item, theproduct from multiplication of the values for the component “variety(A)” is 0.75 (=0.5×1.5) and the product from multiplication of thevalues for the component “variety (B)” is 0.25 (=0.5×0.5). Both productsare lower than the threshold value, so that neither the attribute“variety (A)” nor the attribute “variety (B)” is adopted as theitem-specific recommendation reason for the fifth item.

It will be appreciated from the foregoing description that step S16 isnot performed on the fifth item. By contrast, step S16 is carried out onthe first and the third items. The recommendation reason level regardingthe attribute “variety (A)” or “variety (B)” (variety (A) for the firstitem) is then calculated illustratively as outlined below.

As mentioned above, the values representing the components “variety (A)”and “variety (B)” in the item characteristic vector are allowed to fallwithin the range of 0 through 3. That is, the value can be largerthan 1. The products resulting from multiplication involving the userpreference vector can also be larger than the threshold value “1.” Thatmeans the component values of “variety (A)” and “variety (B)” in theitem characteristic vector or the products involving the user preferencevector cannot be used unmodified as recommendation reason levels on thescale of 0 to 1.

In that case, the recommendation reason determining device 4 in step S16illustratively performs the expression (5) below to normalize theproducts from multiplication of the component values of “variety (A)”and “variety (B)” by their counterparts in the user preference vector,in such a manner that the results will fall within the rage of 0 to 1.The expression is given as follows:

$\begin{matrix}{\begin{matrix}{{{recommendation}\mspace{14mu}{reason}}\mspace{14mu}} \\{{level}\mspace{14mu}{of}\mspace{14mu}{attribute}}\end{matrix} = \frac{{Item}_{value} \times {Usr}_{value}}{{Product}_{\max}}} & (5)\end{matrix}$

In the expression (5) above, Usr_(value) represents the component valueof “variety (A)” or “variety (B)” in the user preference vector, andItem_(value) denotes the component value of “variety (A)” or “variety(B)” in the item characteristic vector of the item of interest. That is,the numerator on the right side of the expression (5) indicates theproduct from multiplication of a given component value by itscounterpart in the user preference vector. In the expression (5), thedenominator Product_(max) stands for the largest of the products frommultiplication of each of the component values of “variety (A)” and“variety (B)” in the item characteristic vectors of all recommendeditems in the recommendation list, by the corresponding one of thecomponent values in the user preference vector.

Obviously, the normalizing technique is not limited to what wasdiscussed above using the expression (5).

In step S19, as shown in FIG. 11, the value “0.7” resulting fromcalculation of the expression (5) above is substituted into the entry onrow 1 (indicating the first item), column 6 (denoting the variety A) inthe recommendation list 22. The value “1.0” from calculation of theexpression (5) is substituted into the entry on row 2 (representing thethird item), column 6 (denoting the variety A). The value “0.93” fromcalculation of the expression (5) is substituted into the entry on row2, column 7 (indicating the variety B).

What has been discussed above are the typical condition serving as thebasis for determination in step S15 regarding the attribute “variety (A,B)” and the typical technique of calculating recommendation reasonlevels for the same attribute in step S16.

As described, the recommendation reason levels adopted as theitem-specific recommendation reasons are written to the recommendationreason list 22. That is, the recommendation reason list is generated instep S19. This completes the process of calculating the recommendationreason level of each item in the example of FIG. 9.

Once step S2 in FIG. 6 (for calculating the recommendation reason levelof each item) is completed, control is passed on to step S3 (forcalculating a common recommendation reason). What takes place in step S3will now be described by referring to FIGS. 12 through 15.

FIG. 12 is a flowchart of detailed steps constituting the process ofcalculating a common recommendation reason level, the process beingcarried out in step S3. In step S31 of FIG. 12, the commonrecommendation reason determining device 5 establishes one of the itemsin the recommendation reason list as the item of interest.

It is assumed here that the above-mentioned recommendation list 22 ofFIG. 11 is utilized for the process. In this case, one of the first, thethird, and the fifth items is established as the item of interest. Forthis example, the first item is assumed to be established as the item ofinterest.

In step S32, the common recommendation reason determining device 5acquires from the recommendation list the recommendation reason levelfor each of the attributes of the item of interest. For any attribute ofwhich the recommendation reason level is not found in the list, thevalue “0” is substituted (i.e., zero is acquired in step S32).

In step S33, the common recommendation reason determining device 5selects one of all attributes.

In step S34, the common recommendation reason determining device 5updates the common recommendation reason level α for the selectedattribute. That is, the common recommendation reason determining device5 in step S34 adds the recommendation reason level β of the item ofinterest to the preceding common recommendation reason level α. Theresulting sum (=α+β) is then taken as the current common recommendationreason level α.

In step S35, the common recommendation reason determining device 5checks to determine whether the common recommendation reasons for allattributes of the item of interest have been updated.

If in step S35 the common recommendation reasons for all attributes ofthe item of interest are not found to be updated yet, control isreturned to step S33 and the subsequent steps are repeated. That is, aloop made up of steps S33 through S35 is executed repeatedly, wherebythe common recommendation reason for each of the attributes is updatedsuccessively.

When the common recommendation reasons for all attributes of the item ofinterest are found to have been updated in step S35, control is passedon to step S36.

In step S36, the common recommendation reason determining device 5checks to determine whether all items included in the recommendationlist have each been established as the item of interest.

If in step S36 not all items in the recommendation list are found to beestablished as the item of interest, control is returned to step S31 andthe subsequent steps are repeated. In this example, a loop formed bysteps S31 through S36 is carried out repeatedly, whereby each of thethird and the fifth items is established successively as the item ofinterest. The common recommendation reason for each of the attributes“color (red),” “color (white),” “variety (A),” and “variety (B)” is thenupdated successively for the third and the fifth items.

In terms of the attributes involved, the loop of steps S31 through S36constitutes a process of calculating the sum total of the recommendationreason levels for each attribute of each recommended item. When stepsS31 through S36 are repeated in a loop, the sum totals of therecommendation reason levels for all recommended items are determinedeventually as the ultimate common recommendation reason levels.

When the ultimate common recommendation reasons are determined asdescribed above, the result of the check in step S36 becomes affirmative(“YES”). Control is then transferred to step S37.

In step S37, the common recommendation reason determining device 5selects the attribute whose common recommendation reason level is thehighest as the theme for all recommended items (i.e., commonrecommendation reason).

Illustratively, the loop of steps S31 through S36 is executed repeatedlyusing the recommendation reason list 22 of FIG. 11. The execution of thesteps provides a vector 23, shown in FIG. 13, made up of the componentswith their values representing the common recommendation reason levels(sum totals) for the attributes 11 (aroma),” “2 (aroma),” “3 (aroma),”“color (red),” “color (white),” “variety (A),” and “variety (B).” Morespecifically, the common recommendation reason level (sum total) is 0.5for the attribute “1 (aroma),” 1.5 for “2 (aroma),” 1.5 for “3 (aroma),”0 for “color (red),” 1 for “color (white),” 1.7 for “variety (A),” and0.93 for “variety (B).”

The common recommendation reason level (sum total) for the attribute“variety (A)” is 1.7, the highest of all the levels. Thus the attribute“variety (A)” is determined as the theme for all recommended items(common recommendation reason) in step S37, as indicated in FIG. 13.

When the common recommendation reason is determined as described abovein step S37, the process in the example of FIG. 12 is brought to an end.That is, the process of calculating a common recommendation reason levelin step S3 of FIG. 6 is terminated, and control is passed on to step S4.

What has been discussed above is the process of calculating a commonrecommendation reason level detailed in FIG. 12. The same process instep S3 of FIG. 6 is not limited to the example of FIG. 12; it may beimplemented using diverse techniques. That is, the technique ofdetermining the common recommendation reason is not limited to what isdescribed in FIG. 12. Any one of different appropriate techniques may beadopted for the purpose.

In the example of FIG. 12, as discussed above, the technique ofdetermining the common recommendation reason involves initiallyobtaining the sum total of the recommendation reason levels for each ofall attributes. The sums are used unmodified as common recommendationreason levels. The attribute corresponding to the highest of the commonrecommendation reason levels is then selected as the commonrecommendation reason. With this technique, each of the attributesinvolved is assigned the same weight. In other words, it is assumed thateach attribute has the same level of importance from the user's point ofview.

In practice, however, different attributes may well have differentlevels of importance for different users. In recent years, some usershave expressed their desire to have more importance attached to aparticular attribute or attributes when items are recommended; othershave wanted to get less importance assigned to a specific attribute orattributes.

These demands of the different users can be met illustratively byadopting the inventive technique of determining a common recommendationreason, as will be outlined below. The technique involves initiallyacquiring a sum total of the recommendation reason levels for each ofall attributes. The sums of the recommendation reason levels for eachattribute are individually weighted to reflect each user's preferredlevel of importance. The weighted sum totals are used as commonrecommendation reason levels, and the attribute having the highest ofthe common recommendation reason levels is determined as the commonrecommendation reason.

More specifically, the technique proposed here may be carried out by thecommon recommendation reason determining device 15 as follows:

The common recommendation reason determining device 15 generates avector made up of the components representing the sum totals of therecommendation levels for each of the attributes involved. This vectorwill be referred to as the sum total vector in the ensuing description.Illustratively, the above-mentioned vector 23 in FIG. 13 (also in FIG.14) is generated as the sum total vector having (0.5, 1.5, 1.5, 0, 1,1.7, 0.93) as its components.

The common recommendation reason determining device 15 prepares inadvance a vector with its component values suitably weighted to reflecteach user's preferred level of importance regarding the attributesinvolved. This vector will be referred to as the effectiveness vector inthe ensuing description. For example, a vector 24 shown in FIG. 14 isprovided as an effectiveness vector. Although the vector 24 is indicatedas a three-dimensional vector in FIG. 14, the vector in practice may beof the same type as the vector 23, i.e., a seven-dimensional vectorhaving (2.0, 2.0, 2.0, 0.5, 0.5, 1.0, 1.0) as its components.

The common recommendation reason determining device 15 multiplies eachof the values denoting the components of the sum total vector 23 by thevalue representing the corresponding component of the effectivenessvector 24. The product from multiplication of the values of eachcomponent is regarded as the common recommendation reason level for thecorresponding attribute. A vector 25 shown in FIG. 14 is then generatedusing the common recommendation levels of the attributes involved as thecomponent values (this vector will be referred to as the commonrecommendation reason level vector in the ensuing description). Whereasin the example of FIG. 13 the vector 23 itself is utilized as the commonrecommendation reason level vector, the vector 23 in FIG. 14 is employedmerely as a sum total vector. As the common recommendation reason levelvector, the vector 25 is adopted using (1.0, 3.0, 3.0, 0, 0.5, 1.7,0.93) as its component values.

The common recommendation reason determining device 15 determines as thecommon recommendation reason the attribute that has the highest of thecommon recommendation reason levels, i.e., the attribute correspondingto the component having the largest value. More specifically, in theexample of FIG. 14 the second and the third components in the commonrecommendation reason level vector 25 take the highest value of 3.0. Thesecond and the third components of the vector 25 denote the attributes“2 (aroma)” and “3 (aroma)” respectively. Thus the attributes “2(aroma)” and “3 (aroma)” are determined as the common recommendationreason. With the attribute “aroma” at level 3 (the larger the value, thestronger the aroma), the common recommendation reason is determined as a“robust aroma” (for wines) as indicated in the recommendation images ofFIGS. 2 through 5.

As described, FIG. 14 serves to explain how the common recommendationreason is determined by use of a technique different from that of FIG.13. The common recommendation reason determining technique is notlimited to the examples of FIGS. 13 and 14. Other suitable techniquesmay be adopted instead.

For example, the vector 24 in FIG. 14 may be replaced as theeffectiveness vector by a vector 26 in FIG. 15. FIG. 15 shows anotherexample of the effectiveness vector with its components representingweighted values of the items involved.

Where the effectiveness vector 26 of FIG. 15 is adopted, the commonrecommendation reason determining technique is practiced as follows:before acquiring a sum total of the recommendation reason levels foreach of the items, the common recommendation reason determining device15 weights the recommendation reason level for each of the attributesregarding each of the items.

More specifically, given that the weighted value (component value) ofthe first item in the effectiveness vector 26 of FIG. 15 is 1.0, therecommendation reason level for each of the attributes of the first itemis weighted by a factor of 1.0 (i.e., the recommendation reason levelsare not updated).

In addition, given that the weighted value (component value) of thethird item in the effectiveness vector 26 of FIG. 15 is 2.5, therecommendation reason level for each of the attributes of the third itemis weighted by a factor of 2.5 (i.e., the recommendation reason levelsare multiplied by 2.5 when updated).

Furthermore, given that the weighted value (component value) of thefifth item in the effectiveness vector 26 of FIG. 15 is 0.5, therecommendation reason level for each of the attributes of the fifth itemis weighted by a factor of 0.5 (i.e., the recommendation reason levelsare multiplied by 0.5 when updated).

In the manner described above, the common recommendation reasondetermining device 15 uses the effectiveness vector 26 to update therecommendation reason levels of the items included in the recommendationreason list.

Thereafter, the common recommendation reason determining device 15carries out the above-described process of calculating the commonrecommendation reason level in FIG. 12 using the updated recommendationreason list.

The series of steps constituting the above process is executed by thecommon recommendation reason determining device 15. This is how anothertypical common recommendation reason determining technique isimplemented by use of the effectiveness vector shown in FIG. 15.

The foregoing examples were shown to determine the common recommendationreason (theme) following determination of the recommended items.Alternatively, another technique of determining a common recommendationreason may be implemented as follows: the attribute preferred by a givenuser is established beforehand as a common recommendation reason on thebasis of that user's history. Recommended items are then determinedbased on the predetermined common recommendation reason.

The alternative technique above corresponds to the common recommendationreason presenting process shown in the flowchart of FIG. 16. FIG. 16with its flowchart outlines a process different from what was discussedearlier in reference to FIG. 6. The process of presenting the commonreason for recommendation as outlined in FIG. 16 will now be described.

In step S51 of FIG. 16, based on the user's history or on other suitableinformation, the common recommendation reason determining device 5 inFIG. 1 establishes the attribute deemed important by the user as thetheme (common recommendation reason) for all items.

It might happen that each of all attributes corresponding to allcomponents of the item characteristic vector is established as the theme(common recommendation reason) in step S51. Which of the attributes isselected is dependent on the establishing technique in use. Diversetechniques of establishing the common recommendation reason may beadopted based not only on the user's history but also on other suitableinformation.

Illustratively, if the effectiveness vector such as the above-describedvector 24 in FIG. 14 is provided in advance, a technique may be adoptedwhereby the attribute corresponding to the largest of the componentvalues in the effectiveness vector is established as the theme.

The technique of determining the component values of the effectivenessvector (i.e., of determining effectiveness) is not limited in any way.For example, an effectiveness determining technique based on the user'shistory of item usages may be adopted. An alternative technique mayinvolve adopting user-entered values as representative of effectiveness.Another technique may involve acquiring values from the calculation ofuser-entered values by a particular method and regarding the acquiredvalues as representative of effectiveness. A further technique maydetermine effectiveness based on a particular algorithm such as thegenetic algorithm. An even further technique may determine effectivenesson the basis of relationships between all users' histories of itemusages on the one hand, and a particular user's history of item usageson the other hand. If the last technique is adopted, it is assumed thatthe items to be handled are wines and that the user in question tends toselect a white whine while the majority of users are likely to choosered wines under a predetermined condition (e.g., a birthday for which awine needs to be selected). On that assumption, the level ofeffectiveness for white wines is raised so as to reflect the user'spreference for white wines.

Yet another technique may involve finding the largest of all values ofthe components that constitute a user preference vector incorrespondence with appellation attributes and establishing as the themethe attribute represented by the component having the largest of thecomponent values. This technique makes use of appellation attributesbecause they are often utilized as the attributes denoting userpreferences.

Another technique may involve taking note of a plurality of itemsincluded in the user's history, acquiring the values representing thecomponents that constitute the item characteristic vector of each of theitems involved in correspondence with numeric attributes, finding thevariance of the component values between the items, and establishing theattribute relative to the smallest variance as the theme. The itemsincluded in the user's history may be the items purchased by the userfrom item-offering (i.e., selling) sites on the Internet or the items ofwhich the evaluation values have been input by the user. This techniqueestablishes the numeric attribute relative to the smallest variance asthe theme because numeric attributes generally denote the levels of anyone of the characteristics of items. Given the attribute having thesmallest variance, it may be said that the user has a consistentpreference for the attribute in question (i.e., the user has purchasedor appreciated the items with their levels close to one another).

Any one of the above-outlined techniques may be adopted and executed instep S51 of FIG. 16. The common recommendation reason determining device5 establishes a common recommendation reason by carrying out the adoptedtechnique. The common recommendation reason thus established is suppliedto the recommendation list generating device 3 (arrow not shown in FIG.1). Control is then passed on to step S52.

In step S52, the recommendation list generating device 3 acquires fromthe item characteristic vector storing device 2 at least one itemcharacteristic vector (of each item) of which the attribute establishedas the theme (i.e., common recommendation reason) for all items has acomponent value satisfying a predetermined condition.

In step S53, the recommendation list generating device 3 calculates thelevel of similarity between the user preference vector in the userpreference vector storing device 1 on the one hand, and the itemcharacteristic vector or vectors acquired in step S52 on the other hand.The recommendation list generating device 3 determines as therecommended items each item corresponding to the item characteristicvector whose level of similarity satisfies a predetermined condition.

The recommendation list generating device 3 forwards the recommendeditems and the theme for all items (common recommendation reason) to thepresenting device 6 (arrows not shown in FIG. 1). Control is then passedon to step S54.

In step S54, the presenting device 6 presents the recommended items tothe user together with the theme for all items (common recommendationreason). This completes the common recommendation reason presentingprocess in the example of FIG. 16.

The series of steps and processes described above may be executed eitherby hardware or by software. In such cases, the recommendation system ofFIG. 1 may be constituted illustratively by at least one personalcomputer such as the one shown in FIG. 17. The foregoing examples arepracticed using the recommendation system of FIG. 1 formed by a singleinformation processing apparatus. That is, one personal computerindicated in FIG. 17 may form the recommendation system of FIG. 1.

In FIG. 17, a CPU (central processing unit) 101 performs diverseprocesses in accordance with programs held in a ROM (read only memory)102 or in keeping with programs loaded from a storage unit 108 into aRAM (random access memory) 103. The RAM 103 also accommodates data thatmay be needed by the CPU 101 in carrying out its processes.

The CPU 101, ROM 102, and RAM 103 are interconnected by a bus 104. Aninput/output interface 105 is also connected to the bus 104.

The input/output interface 105 is connected to an input unit 106, anoutput unit 107, the storage unit 108, and a communication unit 109. Theinput unit 106 is constituted illustratively by a keyboard and a mouse;the output unit 107 by a display device; the storage unit 108 by a harddisk drive; and the communication unit 109 by a modem and/or a terminaladapter. The communication unit 109 controls communications with anotherapparatus (not shown) connected via a network such as the Internet.

A drive 110 is also connected as needed to the input/output interface105. A removable recording medium 111 such as a magnetic disk, anoptical disk, a magneto-optical disk, or a semiconductor memory isloaded as needed into the drive 110. Computer programs retrieved fromthe loaded recording medium are installed as needed into the storageunit 108.

The series of steps and processes described above may be executed bysoftware. For the software-based processing to take place, the programsconstituting the software may be either incorporated beforehand indedicated hardware of a computer or installed upon use over a network orfrom a suitable recording medium into a general-purpose personalcomputer or like equipment capable of executing diverse functions basedon the installed programs.

As shown in FIG. 17, the recording medium carrying the programs isoffered to users not only as a package medium apart from the computerand constituted by the removable medium 111 such as a magnetic disk(including floppy disks), an optical disk (including CD-ROM (compactdisk-read only memory) and DVD (digital versatile disc)), amagneto-optical disk (including MD (Mini-Disk)), or a semiconductormemory, each of the media carrying the necessary programs; but also inthe form of the ROM 102 or the hard disk drive contained in the storageunit 108, each unit accommodating the programs and incorporatedbeforehand in the computer.

In this specification, the steps which describe the programs to beexecuted and stored on the recording medium represent not only theprocesses that are to be carried out in the depicted sequence (i.e., ona time series basis) but also processes that may be performed parallellyor individually and not chronologically.

In this specification, the term “system” refers to an entireconfiguration made up of a plurality of component devices and/orprocessors.

It should be understood by those skilled in the art that variousmodifications, combinations, sub-combinations and alterations may occurdepending on design requirements and other factor in so far as they arewithin the scope of the appended claims or the equivalents thereof.

What is claimed is:
 1. A method comprising: storing item characteristicvectors and a user preference vector, the item characteristic vectorsdescribing items; determining the item characteristic vectors that aresimilar to the user preference vector, the similar item characteristicvectors describing items that are recommended; determining, by aprocessor, a common attribute from the similar item characteristicvectors and the user preference vector, wherein the common attributeidentifies a common recommendation reason of the recommended items basedon a recommendation reason list for the recommended items; and providingthe common recommendation reason with the recommended items for display.2. The method of claim 1, wherein the determining a commonrecommendation reason further comprises: choosing an item-specificrecommendation reason for each of the similar item characteristicvectors; determining a recommendation reason level for the item-specificrecommendation reasons; adding together the recommendation reason levelscorresponding to individual item specific recommendation reasons acrossthe similar item characteristic vectors; and selecting the commonrecommendation reason as one of the item-specific recommendation reasonswith the highest cumulative recommendation reason level.
 3. The methodof claim 2, wherein the item-specific recommendation reasons are chosenif values of the item-specific recommendation reasons are similar tocorresponding attribute values in the user preference vector.
 4. Themethod of claim 2, wherein the recommendation reason level indicates adegree of similarity between the values of the item-specificrecommendation reasons and the corresponding attribute values in theuser preference vector.
 5. The method of claim 2, further comprising:providing the item-specific recommendation reasons for display.
 6. Themethod of claim 1, wherein the user preferences are determined based ona user history.
 7. An information processing apparatus, comprising:means for storing item characteristic vectors and a user preferencevector, the item characteristic vectors describing items; means fordetermining the item characteristic vectors that are similar to the userpreference vector, the similar item characteristic vectors describingitems that are recommended; means for determining a common attributefrom the similar item characteristic vectors and the user preferencevector, wherein the common attribute identifies a common recommendationreason of the recommended items based on a recommendation reason listfor the recommended items; and means for providing the commonrecommendation reason with the recommended items for display.
 8. Theinformation processing apparatus of claim 7, wherein the means fordetermining a common recommendation reason further comprises: means forchoosing an item-specific recommendation reason for each of the similaritem characteristic vectors; means for determining a recommendationreason level for the item-specific recommendation reasons; means foradding together the recommendation reason levels corresponding toindividual item specific recommendation reasons across the similar itemcharacteristic vectors; and means for selecting the commonrecommendation reason as one of the item-specific recommendation reasonswith the highest cumulative recommendation reason level.
 9. Theinformation processing apparatus of claim 8, wherein the item-specificrecommendation reasons are chosen if values of the item-specificrecommendation reasons are similar to corresponding attribute values inthe user preference vector.
 10. The information processing apparatus ofclaim 8, wherein the recommendation reason level indicates a degree ofsimilarity between the values of the item-specific recommendationreasons and the corresponding attribute values in the user preferencevector.
 11. The information processing apparatus of claim 8, furthercomprising: means for providing the item-specific recommendation reasonsfor display.
 12. The information processing apparatus of claim 7,wherein the user preferences are determined based on a user history. 13.A computer readable media storing a computer program, that when executedby a processor causes the processor to perform a method, the methodcomprising: storing item characteristic vectors and a user preferencevector, the item characteristic vectors describing items; determiningthe item characteristic vectors that are similar to the user preferencevector, the similar item characteristic vectors describing items thatare recommended; determining a common attribute from the similar itemcharacteristic vectors and the user preference vector, wherein thecommon attribute identifies a common recommendation reason of therecommended items based on a recommendation reason list for therecommended items; and providing the common recommendation reason withthe recommended items for display.
 14. The computer readable media ofclaim 13, wherein the determining a common recommendation reason furthercomprises: choosing an item-specific recommendation reason for each ofthe similar item characteristic vectors; determining a recommendationreason level for the item-specific recommendation reasons; addingtogether the recommendation reason levels corresponding to individualitem specific recommendation reasons across the similar itemcharacteristic vectors; and selecting the common recommendation reasonas one of the item-specific recommendation reasons with the highestcumulative recommendation reason level.
 15. The computer readable mediaof claim 14, wherein the item-specific recommendation reasons are chosenif values of the item-specific recommendation reasons are similar tocorresponding attribute values in the user preference vector.
 16. Thecomputer readable media of claim 14, wherein the recommendation reasonlevel indicates a degree of similarity between the values of theitem-specific recommendation reasons and the corresponding attributevalues in the user preference vector.
 17. The computer readable media ofclaim 14, further comprising: providing the item-specific recommendationreasons for display.
 18. The computer readable media of claim 13,wherein the user preferences are determined based on a user history.